Last updated date : 14/2/2016

The following are the pre-requisites for learning machine learning. I have collected these resources from various quora answers, books, and MOOCs.

The following are the pre-requisites for learning machine learning. I have collected these resources from various quora answers, books, and MOOCs.

**GitHub Repo**

**https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md**

**https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md**

**https://github.com/owainlewis/awesome-artificial-intelligence**

**https://github.com/prakhar1989/awesome-courses#machine-learning**

__Mathematics :__

__MIT :__

__http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2005/__

__http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/__**Linear Algebra:**

- Khan Academy - Linear Algebra
- MIT - Linear Algebra
- Linear Algebra, Theory, and Applications. Kuttler
- https://class.coursera.org/matrix-002/lecture

**Calculus:**

- MIT - Variable Calculus
- Khan Academy - Differential Calculus
- Khan Academy - Integral Calculus
- Khan Academy - Multivariable Calculus
- Khan Academy - Differential Equations

**Statistics and Probability:**

- Inferential Statistics
- Descriptive Statistics
- http://www.wzchen.com/probability-cheatsheet
- https://www.youtube.com/playlist?list=PLLVplP8OIVc8EktkrD3Q8td0GmId7DjW0 (stat 110 , harvard )

__Programming:__

__Others:__**Machine Learning Books:**

- Elements of Statistical Learning. Hastie, Tibshirani, Friedman
- All of Statistics. Larry Wasserman
- Machine Learning and Bayesian Reasoning. David Barber
- Gaussian Processes for Machine Learning. Rasmussen and Williams
- Information Theory, Inference, and Learning Algorithms. David MacKay
- Introduction to Machine Learning. Smola and Vishwanathan
- A Probabilistic Theory of Pattern Recognition. Devroye, Gyorfi, Lugosi.
- Introduction to Information Retrieval. Manning, Rhagavan, Shutze
- Forecasting: principles and practice. Hyndman, Athanasopoulos. (Online Book)

**Probability Books:**

- Introduction to statistical thought. Lavine
- Basic Probability Theory. Robert Ash
- Introduction to probability. Grinstead and Snell
- Principle of Uncertainty. Kadane

**Books I purchased:**

CMU ML course : http://alex.smola.org/teaching/cmu2013-10-701x/

Tom Mitchell : http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml

Mathematical monk's ML : https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA

Stanford's ML notes : http://cs229.stanford.edu/materials.html

https://www.youtube.com/playlist?list=PLD63A284B7615313A

http://cs231n.github.io/

ML Projects : https://docs.google.com/document/d/1Ph-__LSg6I-BftTY3yBk2erh8hp0Kik12fjj9PUOygM/pub

How to do AI research : http://net.pku.edu.cn/~cuibin/resources/MIT-do-research.pdf

Tom Mitchell : http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml

Mathematical monk's ML : https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA

Stanford's ML notes : http://cs229.stanford.edu/materials.html

https://www.youtube.com/playlist?list=PLD63A284B7615313A

http://cs231n.github.io/

ML Projects : https://docs.google.com/document/d/1Ph-__LSg6I-BftTY3yBk2erh8hp0Kik12fjj9PUOygM/pub

How to do AI research : http://net.pku.edu.cn/~cuibin/resources/MIT-do-research.pdf

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